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She Innovates- Female owner and firm innovation in India

Shreya Biswas

Assistant Professor, Department of Economics and Finance

Birla Institute of Technology and Science, Pilani, Hyderabad Campus

India

shreya@hyderabad.bits-pilani.ac.in

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She innovates- Female owner and firm innovation in India

Abstract

Using data from World Bank Enterprises Survey-2014, we find that having a female owner in

India increases firm’s innovation probability using both input and output indicators of

innovation. We account for possible endogeneity of female owner variable using a two stage

instrumental variable probit model. We find that the positive effect of female owner variable is

observed in the sub-samples of firms with more access to internal funding, young firms and

firms located in regions with no or less crime This study highlights the need to promote female

entrepreneurship as a potential channel for promoting firm innovation in India.

Keywords: Innovation, female owner, firms, India

JEL Codes: O31, O32, J16, D22, G32

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1. Introduction

India is a lower-middle-income economy as per the World Bank classification and innovation

remains a critical tool for growth. The government has long recognized the importance of

entrepreneurship and innovation for promoting growth in the country. The Government of India

(GoI) has launched the India Innovation Fund in 2007 in a public-private partnership model to

mentor innovators from diverse sectors in the country. Recently, the government has also

introduced policy initiatives to promote women's entrepreneurship by enabling female

entrepreneurs to access cheap capital from formal financial institutions. This paper sheds light

on whether promoting female entrepreneurship can also generate benefits for the firm.

The paper considers data from the World Bank Enterprise Survey of 2014 and finds

that firms having female owners in India are more likely to innovate both using output and

input measures of innovation. Our estimation methodology accounts for endogeneity and the

findings are based on two-stage instrumental variable probit models. The sub-sample analysis

suggests that innovation is higher among firms with female owners facing lower internal

funding constraints. This finding is in line with the literature which suggests that female owners

face financing constraints (Cavalluzzo et al., 2002; Farlie and Robb, 2009). When the access

to internal funding is low and the firm has to rely on external finance for funding innovation,

the positive effect of female owners vanishes. Further, younger firms with female owners are

more likely to innovate compared to younger male-owned firms. Young firms with female

owners innovate more to possibly reduce the credit constraint in the future and signal its growth

and revenue potential to the lenders. Finally, we find that firms with female owners located in

regions where either there is no crime or very low crime are more likely to innovate. This

highlights the importance of a conducive local business environment on female owner and firm

innovation relation.

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The contribution of this study is threefold. First, our study extends the debate on how

to promote innovation especially in developing economies. Aubert (2005) finds that promoting

innovation remains a challenge in developing countries given poor law enforcement,

governance and lower human capital owing to the modest educational attainment of people.

We find evidence that promoting female entrepreneurship could be one possible channel that

can foster innovation among firms in developing countries like India. Second, this study

contributes to the literature on female entrepreneurship. There is a growing literature on

constraints faced by female entrepreneurs (Muravyev et al., 2009; Farlie and Robb, 2009;

Cavalluzzo et al., 2002). The literature on positive effects of having females in decision-making

roles has focused on female representation on boards and the performance of large listed firms

(Adams and Ferreira, 2009; Khan and Vieito, 2013; Bennouri et al., 2018). The studies on the

effect of female ownership on large, medium and small firms are still scant in the context of

developing countries. Our paper provides evidence in favor of the positive effect of having a

female owner on innovation of firms in India. The results are similar to the findings of Dohse

et al. (2019) in the context of European countries.

The rest of the paper is organized as follows. Section 2 provides the background literature

on innovation, female owners and firm outcomes, and the theoretical link between gender of

the owner and firm-level innovation. Section 3 describes the data and the variables in the study.

Section 4 elaborates the methodology employed, while Section 5 presents the results. Finally,

Section 6 discusses the implications of the results and concludes.

2. Background literature and research question

2.1 Innovation

Early works by Schumpeter and Arrow referred to either product or process innovation.

Product (service) innovation is related to the introduction of a product that is radically different

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from the existing products or a variant of the existing product in the market. On the other hand,

process innovation refers to improvement in the production technology or any other

technological development that reduces the marginal cost for the producer. In addition to

product and process innovation, organizational and marketing innovation are also two

important aspects that have been considered in the literature. Managerial slack can deter firm-

level innovation (Rosner, 1968). Innovation diffusion depends upon the organization’s ability

and willingness to introduce new products. Wang and Ahmed (2004) define organizational

innovation as “organization’s public ability to introduce new products to market or opening

new markets through innovative processes and behaviour”. The third edition of the OECD Oslo

Manual (2005) defines the four forms of innovation. In addition to these forms of innovation,

several studies have considered the input factor i.e. research and development expenditure of

the firms as a measure of firm-level innovation.

There is evidence that firm-level innovation is related to improved productivity in

developing countries like Pakistan (Wadho and Chaudhry, 2018), among Chinese

manufacturing firms (Feng et al., 2019) and firms in Argentina (Chudnovsky et al., 2006).

Technological innovation is positively related to the performance of Kenyan firms (Chege et

al., 2020). Lee et al. (2019) find that for high technology firms, product innovation is related

to superior firm performance and the effect is stronger for firms investing in marketing

innovation. On the other hand, for lower technology firms, process innovation improves the

performance of firms with organizational innovation. Coad and Rao (2008) find that innovation

may not be important for the growth of an average firm; however, it is crucial for high-growth

firms. Van Stel et al. (2014) find that innovation is positively related to the survival probability

of firms during crisis periods.

Given the various positive spillover effects of innovation, a large body of literature has

examined the various determinants of innovation. Market structure is one of the most debated

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factors affecting innovation. According to Schumpeterian theory, competition is not conducive

for innovation. This view suggests that monopoly will induce investment in R&D as they have

the resources and the benefits of innovation, which is higher future profits will accrue to the

monopolist. On the other hand, according to Arrow (1962), monopoly market structure will

deter innovation due to the replacement effect (see Baker, 2007 for the debate on market

structure and its impact on innovation). Cohen (1995) does not find any consensus in the

empirical literature regarding the relationship between competition in the market and firm

innovation. Firm size is another widely accepted determinant of firm innovation (Cohen and

Klepper, 1996). Hashi and Stojcic (2013) find that large firms are more likely to innovate, but

innovation output falls with firm size. Additionally, the export activity of the firms also

increases the innovation probability of firms due to the learning effect (Roger, 2004).

Bhattacharya and Bloch (2004) find that size, market structure, and trade share matter for

innovation. Access to formal finance is another essential determinant of innovation for firms

in developing countries (Ullah, 2019). Many of the firms in developing economies rely on

informal finance; however, access to formal financial institutions is crucial for the innovation

strategies of firms.

2.2 Female owners and firm outcomes

The extant literature suggests that differences in firm outcomes can be explained by differences

in the gender of the owner. Studies have found that female-owned enterprises are smaller, have

lower survival rate (Farlie and Robb, 2009), have low productivity (Belitski and Desai, 2019),

and are also less profitable (Hardy and Kagy, 2018). The adverse outcomes of female-owned

firms can be attributed to the additional challenges faced by female owners rather than

differences in their abilities. Female owners often face more severe financing constraints

compared to male-owned firms. Cavalluzzo et al. (2002) find that female small business owners

are more likely to be denied a loan than male owners. Female owned small businesses fail to

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obtain loans as they have low growth potential and often lack collateral (Coleman, 2002). The

gender gap in access to credit is also found for medium enterprises in Sub-Saharan Africa

(Hansen and Rand, 2014). Female entrepreneurs also pay higher interest on loans (Muravyev

et al., 2009). The financing constraint faced by female owners can be due to discrimination in

the credit market or a lower social capital of female owners compared to male owners. The

social capital of females tends to be smaller and inferior compared to the social connections of

males (McPherson and Lovin, 1982). The limited social capital of females can affect their

access to finance (Pham and Talavera, 2018).

Studies in corporate finance have also examined the effect of females in top

management teams on various firm outcomes in the context of large listed companies. Adams

and Ferreira (2009) find that having female directors on board improves attendance. There is

evidence that having females in management teams improves the non-financial performance

of large firms in the United States (Chadwik and Dawson, 2018).

2.3 Female owners and innovation

The theoretical relation between female ownership and innovation is complex and can be

explained using multiple economic theories including discrimination theory and the gender

congruity theory, risk-aversion principle, resource dependence theory along with benefits of

diversity theory.

The discrimination theory suggests that female owners face discrimination in the credit

market and have low access to finance. The lower access to capital can have a detrimental

effect on investment in innovation projects. The discrimination can be due to gender congruity,

especially in a patriarchal society like India. Social norms dictate that women are involved in

unpaid household work like child care, elderly care in joint family and preparing food among

others. In patriarchal societies, female entrepreneurs (leaders) can be seen as less favorably

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than male counterparts (Eagly and Karau, 2002). This can make it more difficult for women to

access credit as they are likely to be perceived to be breaking gender stereotypes in society.

However, one can argue that female owners are aware of such discrimination and hence, would

invest more in innovation projects initially to signal their ability and commitment to the loan

approving officer and increase the survival probability and growth potential in the long run.

Hence, when there exists gender-based discrimination in credit markets, the effect of gender of

the owner on innovation remains ambiguous.

On the other hand, studies on determinants of risk aversion suggest that in general

females are more risk-averse compared to males when it comes to investment decisions

(Croson and Gneezy, 2009). However, the study did not find differences in risk aversion of

males and females in the sample of managers and professionals. Later, Adams and Funk (2012)

show that female managers are not representative of the female population and their risk-taking

behavior may not be different than male managers. If the risk preferences do not differ between

male and female managers, then there should not be any difference in innovation observed for

male-owned firms vis-à-vis firms having female owners.

Diversity proponents suggest that having more gender-diverse organizations can

improve firm outcomes as men and women face different social, political and economic

experiences and such diverse experiences improve group outcomes (Robinson and Denchant,

1997). This idea is closely related to the resource dependence theory (Pfeffer and Slancik,

1978), which suggests that having female owners increases the resources available to the firm,

which can improve firm outcomes including innovation. Diverse organizations are found to

innovate more in the marketplace (HBR, 2013). Ostergaard et al. (2011) also find that gender-

diverse organizations are more likely to innovate compared to organizations dominated by

employees of a particular gender. Diaz-Garcia et al. (2013) find that gender-diverse R&D teams

come up with superior innovation ideas compared to male-dominated teams.

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In the recent past, few studies have examined the relationship between gender of the

owner and innovation practices at the firm level. Using a set of emerging countries in Europe,

Dohse et al. (2019) and Na and Shin (2019) find that female ownership increases the firm’s

probability to innovate. Fu et al. (2020) find that female ownership increases the effect of

education and experience on product and process innovation among Chinese manufacturing

firms. In this study, we examine the role of female owners on both input and output based

innovation measures in the context of a patriarchal country like India, where social norms do

not favor paid work for women.

India provides an interesting case study for our analysis as it is one of the few countries

in the world that has witnessed an overall fall in female labour force participation (Andres et

al., 2017) and stagnation in female labour force participation in urban areas (Klasen and Pieters,

2015) in the past few decades. Even though the Indian economy witnessed decent growth since

the 1990s, this was not substantiated with an increase in participation of females in the labour

force (Lahoti and Swaminathan, 2016). We believe that female owners in India are not

representative of the underlying female population and have already broken gender stereotypes

by exhibiting entrepreneurial ambitions. These firms with female owners are in a position to

reap the benefits of diverse management and decision-making team, which is likely to be

conducive for firm-level innovation outcomes. Further, firms having female owners are also

likely to face more severe credit constraints. In Indian patriarchal society, female owners will

exhibit a tendency to innovate to overcome credit market discrimination and ensure access to

finance. The two effects together suggest that having a female owner can be positively related

to firm-level innovation in India.

3. Data and variables

3.1 Data

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We consider the World Bank Enterprise Survey India dataset for the year 2014 (WBES). It is

a firm-level survey conducted between June 2013 and December 2014. The survey collects

data from 9,281 small, medium and large companies using stratified random sampling

techniques. The survey provides a snapshot about the business environment in the country. It

covers firms operating in the manufacturing, retail and non-retail services industries in 23 states

of India. For selecting the manufacturing units, the Annual Survey of Industries (2013)

collected by the Central Statistics Office of the Ministry of Statistics and Programme

Implementation was used as the sample frame. For services firms, the survey used industry

association lists. The data was collected by Nielsen India on behalf of the World Bank and the

mode of data collection was face to face interviews. The survey collects information regarding

firm characteristics, sales, gender of the owner, innovation and other performance measures.

3.2 Variables

We consider both output and input measures of innovation as dependent variables in our study.

The output-based innovation measures are given by the enterprise-level process, organizational

and marketing innovation indicators. Product innovation is a dummy that takes the value one

if the firm launched a new product or service during the last three years and zero otherwise.

Process innovation is a dummy that takes the value one if the firm introduces either (a) a new

method of manufacturing or providing services; or (b) improved the logistics and distribution

methods; or (c) improved supporting activities like maintenance systems, accounting or

computing operations and zero otherwise. Organizational innovation is a dummy variable that

takes the value one if the firm has introduced any new organizational structure or management

practices and zero otherwise. Marketing innovation is a dummy that takes the value one if the

firm introduced new methods to market its products or services during the last three years and

zero otherwise. We consider R&D as a proxy for firm-level input measures of innovation. R&D

is a dummy that takes the value one if the firm had spent on R&D activities during the last

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three years or if the firm gives time to its employees to develop or improve new approaches,

products or ideas and zero otherwise.

The main interest variable is the female dummy takes the value one if at least one of

the owners of the firm is a female and zero otherwise. There are 9,224 firm observations for

which information regarding the gender of the owner is available. In our sample, 1,372 firms

have at least one female owner.

We control for size, age, export orientation, being a part of a multi-firm establishment

and financing constraints faced by the firm. We use the WBES size definition wherein the firms

with less than 20 employees are considered small firms, firms with 20-99 employees are

considered medium-sized firms and firms with more than 100 employees are considered as

large firms. Age of the firm is given by the year of interview minus the year of incorporation.

Export orientation of a firm is a dummy if the firm is an exporting firm and zero otherwise.

The multi-firm variable is a dummy that takes the value one if the firm is a part of a large

multiform establishment and zero otherwise. Financing constraint is a categorical variable that

ranges from zero to four, where zero indicates that the firm does not face any difficulty in

accessing finance and four indicates that the firm faces a very severe obstacle in accessing

finance. We also control for state and 26 industry dummies to control for any state-specific

laws and industry-specific regulations or general business environment that can affect the

likelihood of firm-level innovation.

4. Methodology

Given the binary nature of our dependent variable we estimate probit model to understand the

relation between firm-level innovation and female owner dummy. However, it is possible that

firms that have female owners are different than the firms without female owners and our

estimated 𝛽1 captures the effect of these differences due to selection bias rather than the effect

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of female owner on innovation. In order to address the endogeneity related to the female

dummy variable we follow a two-stage instrumental variable probit approach. An instrument

is a variable that should satisfy the relevance and the exogeneity conditions. The relevance

criteria suggest that the instrument is correlated to the female owner variable and the

exogeneity condition implies that the instrument should be otherwise uncorrelated to the

innovation measures.

We consider the state-industry share of firms that have female owners as an instrument.

We consider the theoretical framework suggested by Fisman and Svensson (2007) to construct

our instrument. Fisman and Svensson (2007) argue that firms would get engaged in bribery

payments either because the firm itself is corrupt or because of average industry factors.

Extending a similar argument, we believe the likelihood that the firm will have a female owner

can be driven by two factors i.e. specific firm factors and average business environment given

by industry and state factors. For example, there are few industries that tend to be favored by

females, such as textiles, hospitality etc. Similarly, in India there could be state-specific

programs that encourage female entrepreneurship. Hence, a hospitality firm in a state with a

policy that encourages female entrepreneurship is more likely to have female owners compared

to a mining firm located in a state with no policy targeting female entrepreneurs. Further, a

higher share of firms having at least one female owner in a particular industry within a specific

industry can also increase the firm i’s probability of having a female owner through peer effect.

Finally, we believe that the state-industry specific share of firms having female owners is not

likely to affect the firm-level strategy to invest in innovation. While calculating the state-

industry share of firms having female owners we exclude firm i. In the first stage, we regress

our female owner dummy on the state-industry share of female firms (instrument), firm

controls, industry and state dummies.

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𝐹𝑒𝑚𝑎𝑙𝑒 𝑜𝑤𝑛𝑒𝑟𝑖𝑗𝑠 = 𝛼0 + 𝛼1𝑠𝑡𝑎𝑡𝑒 − 𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝑠ℎ𝑎𝑟𝑒 𝑜𝑓 𝑓𝑒𝑚𝑎𝑙𝑒 𝑜𝑤𝑛𝑒𝑟𝑠𝑖 + 𝜕𝑋𝑖𝑗𝑠 +

∅𝐼𝑛𝑑𝑢𝑡𝑟𝑦𝑖 + 𝜕𝑆𝑡𝑎𝑡𝑒𝑠 + 𝑒𝑖𝑗𝑠 (1)

Equation (2) is estimated using Ordinary Least Square (OLS) method. When our interest

variable is the female dummy, we assume a linear relation in the first stage. In the second stage,

we estimate a probit model wherein the fitted values obtained from stage one is included as an

explanatory variable along with other covariates given by the equation below:

𝐿𝑖𝑗𝑠 = 𝑏0 + 𝑏1𝐹𝑒𝑚𝑎𝑙𝑒 𝑜𝑤𝑛𝑒𝑟̂𝑖𝑗𝑠 + 𝜏𝑋𝑖𝑗𝑠 + 𝜋𝐼𝑛𝑑𝑢𝑡𝑟𝑦𝑖 + 𝜔𝑆𝑡𝑎𝑡𝑒𝑠 + 𝜖𝑖𝑗𝑠 (2)

5. Results

5.1 Summary statistics

The sample characteristics are presented in Table 1. We consider the strict weight reported by

WBES data for computing the sample means. Column1 presents the summary for the full

sample. Among the firms for which the product innovation question was answered, 44.9% did

introduce a new product or service in the last three years. Around 56.4% firms introduced some

process innovation in the past three years preceding the survey. The proportion of firms that

have introduced organizational or marketing innovation or engaged in R&D were less than

50%. Only 10.7% firms had female owners and the rest of the firms had male owners.

<<Insert Table 2 here>>

Columns 2-3 summarize characteristics separately for firms that have female owners

and those without any female owners. We observe that a higher share of firms with female

owners innovate compared to male-owned firms. Female owned firms are also older and are

more export oriented. Among the small and medium firms, less than 10% have female owners,

whereas for large firms close to 19.5% have female owners. This can be due to the fact that

many large firms are part of business groups where the female members from the promoter

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family could be an owner of the firm. As a result, the share of firms having a female owner

seems to be higher for large firms. The univariate statistics suggest that gender of the owner

may be related to the innovation practices of firms. However, other firm characteristics also

differ across male owned and firms with female owners, we consider a multivariate framework

to test our hypothesis.

5.2 Main results

Table 2 presents the coefficients of female owner dummy on various firm innovation obtained

from probit estimation. We find that the female dummy is unrelated to product innovation

(column 1), but is positively related to all other forms of innovation (columns 2-5). Our initial

multivariate results indicate there is an association between having a female owner and

innovation practice of firms in India.

<<Insert Table 2 here>>

As discussed in the methodology section, probit estimates are likely to be biased if

female owner dummy is endogenous. We employ a two-stage IV probit model wherein the

instrument is the state-industry share of female owners. Table 3 presents the coefficients from

the second stage probit model of innovation on predicted female owner variable obtained from

stage one. Female dummy is positive and significant in all the specifications (columns 1-5) for

output and input based innovation measures except product innovation. Further, in the first

stage regression, the coefficient of state-industry share of female is positive and highly

significant in both the specifications suggesting that the instrument is indeed correlated with

the endogenous variable. Our results suggest that having a female owner can improve both

input and output-based innovation practices for Indian firms. The results are in line with the

findings of Dohse et al. (2019) in the context of European countries.

<<Insert Table 3 here>>

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5.3 Heterogeneous effects

5.3.1 Availability of internal funds

Financing of innovation at the firm level is a major challenge faced by owners and managers.

The under-investment in R&D is tied to financing challenges faced by firms. Innovation

projects are high-risk projects with low to moderate expected return and a low probability of

very high return in the future. The high cost of capital deters firms from investing in innovation

projects. Studies suggest that large firms often prefer internal funding for innovation

investments (Hall, 2002; Hall and Lerner, 2009).

In line with the financing argument, we separately analyze the effect of having female

owner for sub-samples based on the share of working capital and fixed asset expenses funded

by internal funds. If the share of working capital and fixed assets that are funded by internal

funds is greater than the median level (40%), the firm is considered as less financially

constrained; otherwise, it is considered to have access to low or moderate internal financing

resources. Figure 1 indicate that the positive effect of female owner on innovation is driven by

the sub-sample of firms having sufficient internal funding and female owners do not have any

effect on innovation for firms that have low internal funds. This could be due to the fact that

firms with female owners with low internal funds have to rely on external funds for funding

innovation projects. Several studies highlight that female entrepreneurs face more severe

external financing constraints compared to male-owned firms (Asiedu et al., 2013; Sabarwal

and Terrell, 2008). Hence, external credit market distortion could be a plausible reason why

the positive effect of female ownership vanishes for firms with lower internal funds.

<<Insert Figure 1 here>>

5.3.2 Young versus old

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The extant literature suggests that the firm age can be related to innovation. Huergo and

Jaumandreu (2004) show that for manufacturing firms, the new entrants are most likely to

innovate compared to older firms. Later, Balasubramanian and Lee (2008) find that the age of

firms adversely affects the technical innovation of firms given by patents. Younger firms are

likely to engage in riskier innovation practices and generate employment (Coad et al., 2016).

Other studies have highlighted that age can improve the innovative capacity of the firms.

Recently, Petruzzelli et al. (2018) also find that older firms are in a better position to apply

mature knowledge and innovate in the market compared to younger firms. However, when it

comes to applying new knowledge, the younger firms are in a more beneficial position.

Cucculelli, (2018) finds that for Italian firms, firm age is positively related to the probability

of product innovation.

We define young firms as those which are in the market for ten years or less and rest

are categorized as old firms. Based on this definition, 38% firms in our sample are young.

Among young firms, 10% have female owners, while 11% old firms have female owners. We

estimate the IV probit models separately for the sample of old and young firms. Figure 2 shows

that the positive relation between female owner on innovation is driven by younger firms in

the sample. For the sub-sample of older firms, there is no relation between the gender of the

owner and likelihood of innovation. It is possible that the femaleinnovate in the initial years to

increase their chance of survival and also high innovation in the start-up phase by female

owners can improve their credit market outcomes in the later part of the firm’s life-cycle.

<<Insert Figure 2 here>>

5.3.3 Crime and innovation

Matti and Ross (2016) provide a review of how crime can be related to entrepreneurial activity

in various settings. Crime is a cost to business activity (Kuratko et al., 2000) as it increases the

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perceived loss on account of theft, robbery, etc. Rosenthal and Ross (2010) find that crime in

the neighborhood has an impact on location decisions of business in the United States. A higher

crime rate in the city tends to deter business activity and entrepreneurs would prefer relocating

to safer places to ensure a lower effect on revenues. There is a possibility that high-quality

firms self-select to locate themselves in low crime rate locations. These high-quality firms are

likely to be innovators. The study by Velasquez (2019) shows that the crime rate has an adverse

effect on self-employment and women’s labour supply in Mexico. One can argue that if the

crime rate is high, the uncertainty in business activity is even higher and this can have a

negative effect on investments in high-risk innovation projects. We separately analyze whether

the positive effect of female ownership on innovation practices is different for firms based on

the crime in the neighborhood.

The WBES specifically asks respondents, “how much of an obstacle is crime, theft and

disorder for the business?” If the respondent answers no or minor obstacle for the above

question, we consider that the firm is in a low crime region. In case the response is moderate,

major and very severe we classify the firm as situated in a high crime region. This suggests the

possibility of crime affecting the location decisions of firms. Based on this classification,

around 14.5% of firms are in high crime regions and the rest are in low crime regions. Almost

13.25% firms in the low-crime region have female owners and less than 10.5% firms in the

high-crime region have female owners. We observe that in the high-crime region, lesser firms

are likely to have female owners. A high crime region is likely to reduce physical mobility and

hours spent by females outside the household. Figure 3 indicates that female owners increase

the likelihood of innovation among firms located in low crime regions; however, there is no

effect of female ownership on the probability of innovation for firms in high crime regions. It

appears that environmental factors like low crime are important to reap the positive effect of

female owner on firm outcomes. Becker and Rubenstein (2011) model of response to crime

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can be extended to explain the insignificant result of female ownership for high crime sub-

sample. The perceived risk of victimization by female owners in high crime regions can

negatively affect their involvement in businesses and make them more risk-averse, which in

turn may reduce investments in innovation projects.

<<Insert Figure 3 here>>

6. Discussions and conclusion

In this study, we find that firms having female owner are more likely to innovate in India after

accounting for the possibility of endogeneity. The results are driven by firms with more internal

funding resources, younger firms in the sample and firms located in regions with no or low

crime. The findings have implications for both future research and policy-making.

The Economic Survey (2020) suggests that entrepreneurship activity is positively

correlated with GDP growth in the country, and India ranks third in terms of new firm creation.

The GoI initiated the Start-up India initiative in January 2016 to promote entrepreneurship and

innovation in the country1. Under this initiative, the start-ups are tax exempted for three years.

Such policy initiatives are a step in the right direction. Our study suggests that additional

initiatives to bridge the gender gap in entrepreneurial activity can serve the twin objectives of

empowering women and promoting innovation. The Indian state of Telangana started an

incubator in 2018 called WE Hub to promote women entrepreneurship in the state2. Additional

efforts at the state and national level to formally promote and nurture female entrepreneurs,

especially during the first few year post registration are critical for survival and firm-level

innovation. Policymakers may consider providing R&D subsidies for start-ups in general and

specifically for female owners.

1 https://www.startupindia.gov.in/ 2 http://wehub.telangana.gov.in/

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Our study highlights that firms with female owners having access to internal funding

are more likely to innovate. It is possible that access to external capital remains a crucial

challenge for female owners. There are initiatives like Stree Shakti package3 and Udyogini

scheme4 that provide access to capital, marketing, training and other support to women

entrepreneurs in the country. However, it is imperative to promote such schemes among the

target population by conducting roadshows and address social norms to reduce discrimination

among women in the credit market. Further, our results also highlight the importance of

maintaining good law and order as that has an effect on the business location as well as strategic

decisions like innovation investment by female owners. The study provides evidence that

promoting female entrepreneurship in developing economies can initiate a virtuous cycle of

wealth creation through innovation along with the socially desirable outcome of reducing

gender-gap in outcomes.

3 https://sidbi.in/oldsmallb/bank-schemes/stree-shakti-package 4 http://www.udyogini.org/

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Reference:

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27

Tables

Table 1: Summary statistics

The table below presents the summary statistics for innovation outcomes, female ownership variables and other

firm characteristics.

Full sample Firms with female

owners

Male-owned firms

Product innovation 0.449 0.498** 0.440

Process innovation 0.564 0.610*** 0.552

Organizational innovation 0.417 0.427* 0.409

Marketing innovation 0.464 0.488*** 0.455

R&D 0.438 0.495*** 0.424

Female 0.107 1.000 ----

Share of female 4.654 48.418 ----

Size(share):

Small 0.461 0.376 0.474

Medium 0.404 0.387 0.409

Large 0.135 0.236 0.117

Age(in years) 16.157 19.485*** 15.911

Multifirm(share) 0.787 0.785 0.793***

Financial Constraint(share):

No Constraint 0.341 0.419 0.326

Less Constraint 0.333 0.247 0.345

Moderate Constraint 0.175 0.171 0.178

Major Constraint 0.106 0.108 0.108

Severe Constraint 0.044 0.056 0.044

Export(share) 0.089 0.221*** 0.074

No. of observations 9281 1372 7852

*** p<0.01, ** p<0.05, * p<0.1

28

Table 2: Female ownership and innovation- Probit model

The table below presents the coefficients of probit model of innovation outcomes on female owner dummy and

other firm characteristics. *** p<0.01, ** p<0.05, * p<0.1

(1) (2) (3) (4) (5)

Product

innovation

Process

innovation

Organizational

innovation

Marketing

innovation

R&D

Female

owner

0.080* 0.159*** 0.103** 0.115*** 0.174***

(0.042) (0.044) (0.043) (0.043) (0.044)

Medium 0.297*** 0.274*** 0.127*** 0.074** 0.214***

(0.034) (0.034) (0.035) (0.035) (0.036)

Large 0.380*** 0.383*** 0.176*** 0.169*** 0.323***

(0.044) (0.047) (0.046) (0.045) (0.047)

Age 0.001 0.001 -0.001 -0.002 0.001

(0.001) (0.001) (0.001) (0.001) (0.001)

Multi firm -0.090** -0.247*** -0.399*** -0.318*** -

0.328***

(0.038) (0.040) (0.039) (0.039) (0.040)

No/ Less

constraint

0.110*** 0.296*** 0.288*** 0.223*** 0.323***

(0.039) (0.040) (0.040) (0.040) (0.041)

Moderate

constraint

-0.045 0.244*** 0.370*** 0.377*** 0.432***

(0.044) (0.045) (0.045) (0.045) (0.047)

Major

constraint

0.031 0.236*** 0.293*** 0.355*** 0.250***

(0.056) (0.056) (0.056) (0.056) (0.059)

Severe

constraint

-0.166** 0.008 0.089 0.074 0.012

(0.077) (0.078) (0.076) (0.076) (0.076)

Constant -0.423*** 0.529*** 0.591*** 0.071 -

0.581***

(0.090) (0.097) (0.097) (0.093) (0.093)

State FE Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes

Observations 9,037 9,032 9,029 9,029 9,004

29

Table 3: IV probit regression output.

The table below presents the coefficients of instrumental variable probit model of innovation outcomes on

female owner dummy and other firm characteristics. *** p<0.01, ** p<0.05, * p<0.1

(1) (2) (3) (4) (5)

Product

innovation

Process

innovation

Organizational

innovation

Marketing

innovation

R&D

Female owner 0.024 0.155*** 0.125** 0.194*** 0.206***

(0.056) (0.056) (0.053) (0.055) (0.057)

Medium 0.305*** 0.273*** 0.122*** 0.069** 0.216***

(0.034) (0.035) (0.035) (0.035) (0.037)

Large 0.387*** 0.378*** 0.171*** 0.163*** 0.319***

(0.045) (0.047) (0.046) (0.046) (0.048)

Age 0.001 0.001 -0.001 -0.002* 0.002

(0.001) (0.001) (0.001) (0.001) (0.001)

Multi firm -0.083** -0.239*** -0.389*** -0.306*** -0.319***

(0.038) (0.041) (0.039) (0.039) (0.041)

No/ Less

constraint

0.115*** 0.293*** 0.287*** 0.220*** 0.316***

(0.039) (0.040) (0.040) (0.040) (0.042)

Moderate

constraint

-0.044 0.234*** 0.360*** 0.374*** 0.425***

(0.044) (0.046) (0.045) (0.045) (0.047)

Major constraint 0.030 0.222*** 0.283*** 0.349*** 0.245***

(0.056) (0.056) (0.057) (0.057) (0.059)

Severe constraint -0.173** 0.007 0.090 0.076 0.011

(0.077) (0.078) (0.077) (0.077) (0.077)

State-industry

share of females

0.016*** 0.016*** 0.016*** 0.016*** 0.016***

(0.000) (0.000) (0.000) (0.000) (0.000)

Constant -0.429*** 0.533*** 0.597*** 0.063 -0.588***

(0.090) (0.097) (0.097) (0.093) (0.093)

State FE Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes

Observations 8,915 8,910 8,907 8,907 8,882

30

Figures

Figure 1: Female owner and innovation: High versus low internal funding

The figure below presents the coefficients obtained from second stage IV-probit regression of innovation on

female owner and other firm characteristics for the sub-sample of firms with low and high internal funding

resources.

31

Figure 2: Female owner and innovation: Young versus old firms

The figure below presents the coefficients obtained from second stage IV-probit regression of innovation on

female owner and other firm characteristics for the sub-sample of old and young firms in the sample.

32

Figure 3: Female dummy and innovation: Low versus high crime region

The figure below presents the coefficient obtained from second stage IV-probit regression of innovation on

female owner dummy for the sub-sample of firms in low and high crime region.